Dr. Youakim Badr, associate professor of data analytics, teaches courses in data mining, deep learning, predictive analytics, and analytics programming. Dr. Badr is interested in developing a new chain of data analytical models, tools, and platforms for designing and deploying trustworthy AI service systems by leveraging his research activities built around service computing and smart services for the IoT. He is a member of Linux Foundation for AI and Data (LFAI&Data).
Dr. Adrian S. Barb, associate professor of information science, teaches databases, data mining, and big data courses. He has worked as a database programmer analyst as well as a web developer at University of Missouri. His research interests include data mining, knowledge discovery in databases, knowledge representation and exchange in content-based retrieval systems, semantic modeling and retrieval, conceptual change, ontology integration, and expert-in-the-loop knowledge generation and exchange.
Dr. Phillip A. Laplante, professor of software and systems engineering, pioneered the area of real-time image processing, co-founding the first journal and publishing the first two texts on the subject. For these achievements, he was named a Fellow of SPIE. In AI, he has investigated uncertain information processing using fuzzy sets and rough set theory and, more recently, has focused on the use of AI in safety-critical systems. He holds an appointment as a computer scientist in the Secure Systems and Applications group at the National Institute of Standards and Technology (NIST), working on the IoT, blockchain, and related technologies.
Dr. Partha Mukherjee, assistant professor of data analytics, teaches courses in analytics programming, data mining, predictive analytics, and analytics systems design. He is a member of ACM, ACEEE, AIS, AiR, and ASE, and has published papers in peer-reviewed IEEE, Elsevier, and ACM Journals and conferences. Dr. Mukherjee’s research interests include social computing, web analytics, data mining, e-commerce, and natural language processing with a focus on text simplification.
Dr. Ashkan Negahban, assistant professor of engineering management, performs research on stochastic simulation, statistical data analysis, and optimization techniques that advances the science of decision-making in a wide range of applications, including manufacturing, sharing economy, and supply chains. He also conducts research on the use of machine learning (ML) in simulation models as well as training and testing ML/AI algorithms via simulations. His research has been supported by the NSF, Google, Microsoft, and multiple research institutes at Penn State.
Dr. Colin Neill is a professor of software engineering and systems engineering and the head of the MPS in Artificial Intelligence program. He has an extensive background in the design, architecture, and analysis of complex systems. His AI–related work includes industrial applications of machine vision and expert systems; applications of fuzzy sets and rough set theory to uncertainty in software engineering; individual and team cognition processes; network analytics; and text mining and natural language processing of social media.
Dr. Robin G. Qiu is a professor of information science. He teaches courses on data analytics, information science, software engineering, and cyber security. His research includes data and computational sciences, health-care analytics, smart service systems (health care, city mobility, energy efficiency, IoT, etc.), blockchain, and cybersecurity analytics. He served as the editor-in-chief of INFORMS Service Science and as an associate editor of IEEE Transactions on Systems, Man, and Cybernetics and IEEE Transactions on Industrial Informatics and has more than 170 publications.
Dr. Dusan Ramljak, assistant teaching professor of information science, teaches courses on information science, data science, storage systems, and emerging technologies. He has been applying data science on storage systems in NSF IUCRC projects with HPE, Dell, Huawei, and other companies and has more than 20 years of system administration experience facilitating business and research in the U.S., Portugal, and Serbia. His research interests include solving challenging storage systems, provenance, and caching problems, and developing and integrating distributed and parallel data mining and statistical learning technology for an efficient knowledge discovery at large sequence and temporal databases.
Dr. Raghvinder S. Sangwan is an associate professor of software engineering and director of the Big Data Lab — a research collaborative focused on data science and artificial intelligence and their applications. His own research in this space focuses on network analytics approaches to large-scale complex systems, explainable AI, and the design of secure AI systems.
Dr. Satish Srinivasan is an associate professor of information science. He teaches courses in data retrieval, processing, storage, and mining; predictive and prescriptive analytics; and the application of analytics to particular domains, including cybersecurity and sport. His research interests include natural language processing and text mining of social media; network analytics techniques to determine critical elements in large-scale networks; and the application of machine learning to bioinformatics and genomics.
Dr. Xi Zhang is an assistant professor of data analytics and teaches courses in data mining, predictive analytics, and Python programming. Her research focuses on statistical and machine learning approaches to financial modeling and prediction as well as portfolio management and optimization.
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